Extracting Conditionally Heteroskedastic Components using Independent Component Analysis

DOI10.1111/JTSA.12505zbMath1447.62104arXiv1811.10963OpenAlexW2902232607WikidataQ96168769 ScholiaQ96168769MaRDI QIDQ5111846

Markus Matilainen, Klaus Nordhausen, Sara Taskinen, Jari Petteri Miettinen

Publication date: 27 May 2020

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.10963

zbMATH Keywords

asymptotic normalityblind source separationautocorrelationARMA-GARCH processprincipal volatility component

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Stationary stochastic processes (60G10) Economic time series analysis (91B84)

Related Items (4)

Stationary subspace analysis based on second-order statistics ⋮ Blind source separation for compositional time series ⋮ On the usage of joint diagonalization in multivariate statistics ⋮ Modeling temporally uncorrelated components of complex-valued stationary processes

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